Historically, “companies” (a term defined below) and their customers often have done business across a gap, so to speak. Product or service offerings by a company and the customers' desired product or service do not fully match. In part, this gap is a manifestation of the facts that (1) companies have an incomplete grasp of customer needs, their relative preferences and the pricing utilities customers attach to those preferences (which utilities, equating to the customer's willingness to pay, are dynamic) and (2) a company's costs, profits and inventory (which may control what it can offer on a timely basis) are also dynamic. However, it is also in major part a manifestation of the lack of information technology tools, which can close the gap. To collect dynamic customer and company data and then employ those dynamic data to close the gap is a complex technical problem.
Generally, the customer is treated as an individual and sales terms are customized only when the cost of negotiation is justified—for very large transactions. Many products and services, though, represent complex, multi-faceted offerings and customers weigh their preferences for product features differently at different times. A customer might care more about cost one day and more about availability or delivery time or warranty if queried a few days or weeks later, to use some basic trade-offs as examples. Generally, a company's product consists of many value elements, (explained later) all of which are bundled together to be sold as a single product. But, not every customer values all the aspects of a product equally or needs all. Every customer places a different value (which may be a function of time and situation) on each aspect of a product. With features bundled together in a product, companies end up either incurring costs to sell something to a customer that he does want or lose a customer because the extra undesired value elements forced the product price too high for the customer.
The underlying problem is both that customer demands are incompletely understood and that such demands can change quickly, whereas a company's productive capacity or service often does not have the same dynamic time frame and is supported by a relatively fixed (in the short term) capacity and supply chain.
A company typically uses demand forecasts to build product quantities to match demand. However, companies' forecasts often prove imperfect, leading to shortage or excess supply in one or more product types. If a product has been sold to a customer, the sold quantity of product, generally, cannot be resold to another potential customer. However, it is possible that is another potential customer who may offer a higher value for a product that has been sold. Such situations may lead to potential opportunity loss for the company, especially, if the potential customer spills over (i.e., leaves the company and goes elsewhere, such as to a competitor). In some situations, such spilled customers may be high paying customers, thus, leading to a “high value spill”. The situation becomes worse when the former customer returns the purchased product. The company may, thus, be left with the unsold (returned) product and loses an opportunity to sell. To overcome such situations, companies in some industries like airlines, hotels, car rental and so forth, oversell their products (i.e., sell more than the supply).
Consider the airline industry, where overbooking (or overselling) is very commonly practiced by several airlines across the world. To hedge against last minute cancellations and no-shows (collectively referred to as CNS) and to save high revenue spill, airlines overbook their flights (i.e., sell more tickets than the flight capacity). A no-show customer is defined as a customer with a confirmed ticket who does not turn up for a flight. As described above, the term “High revenue spill” refers to potential revenue loss from potential high revenue paying customers who want to buy a ticket on a flight, but, may spill over to a competitor if the desired flight is not available. The airlines usually try to sell tickets (often, at high prices), even after reaching flight capacity, to not let go of any such potential high revenue paying customers. If the number of people who turn up for the flight is more than the flight capacity (i.e., a situation termed “oversale”), the airlines try to bump customers (i.e., shift customers out of their currently booked flight) voluntarily and/or involuntarily. Airlines use various incentives to bump customers, such as travel vouchers, upgrades, various coupons and likewise. Consider an example. A flight has capacity of 100 seats, however, an airline overbooked 110 customers on that flight, since they expected CNS to be 10. Since, it is difficult to estimate CNS accurately, often, airlines face an oversale situation, i.e., more (>100) customers turn up for the flight, or a “spoilage” situation (i.e., fewer than 100 customers turn up). An oversale situation results in costs and customer ill-will that may increase exponentially with the increase in the number of bumped customers. A spoilage situation may reflect on loss of potential revenue from spill.
On the other side of the screen, there is a significant portion of customers who are price sensitive, and might be willing to shift from their booked flights to other flights in return for desired incentives. For instance, in the airline industry, the customers usually buy tickets one to four weeks in advance (of the premeditated travel date) to get the low fares since the fares, normally increase as the departure date of the flight approaches. They can shift/move their choice of utilizing a product to a reasonable extent if they are rewarded. In this way, those customers may trade-in their flexibility in product utility.
From the above discussion, it is clear that flexibility of customers may be mapped or utilized to satisfy the fixed (or less flexible) demand of other customers. In the context of the airline industry, the flexibility from some customers may be mapped or utilized to satisfy fixed (or less flexible) demand of other customers. But so far, there is no existing system and method, which can allow a company to accomplish this optimally.
Today, airlines do not have any mechanism to allow such flexibility or changes in customer tickets at an individual level at conditions that would optimally satisfy both the parties. Instead, airlines try to deal with all such customers in a rather fixed way (or one bumping/overbooking policy) leading to customer ill-will, high oversale costs and opportunity costs from potential revenue spill (and unsatisfied customer demand). Besides the airline industry, there are several other industries (as mentioned above) that either do not allow flexibility or follow processes that involve high costs and/or demand significant efforts on the customer's end.
What is needed is a mechanism that allows a company to map varying product flexibility across different customers in way that concurrently optimizes value for the company and customers. Indeed, there is no system or method available that can be applied to all the above industries, and many more, and help companies to match the availability of their products to their customers' preferences, let alone while concurrently maximizing the benefits to both the company and its customers.
A technology platform (i.e., system) and methodology thus are needed for customizing, in an optimal way, a match between customers' desire to trade-in flexibility and the company value. In the context of airline industry example, a technology platform (i.e., system) and methodology thus are needed for customizing, in an optimal way, a match between customers' desires to trade-in their travel flexibility and airline's value.
More particularly, a system and methodology are needed which support optimal customization of service offerings in the airline and other industries. If such a match could be made, both company and customer would benefit. The customer would be more satisfied and the company (both in short term and long term) will be more profitable. A win-win scenario is created rather than a zero sum game.